近年來雲嘉南空品區空氣品質不良率以臭氧為指標污染物之比例由1994年12%至2005年增加為53.6%，顯示臭氧於雲嘉南地區已逐漸取代懸浮微粒成為主要之指標污染物，且僅次於高屏空品區為第二污染嚴重之空品區。本研究使用環保署雲嘉光化測站(PAMS)之2007年6月至2008年12月連續監測資料，以年平均濃度、最大反應增量 (Maximum incremental reactivity, MIR)及丙烯當量濃度(Propylene-equivalent)三種指標探討主要VOCs物種排序，利用化學質量平衡受體模式(Chemical mass balance, CMB8.2)來推估臭氧事件日與非事件日時污染源對受體點VOCs之貢獻量，並以MIR理論瞭解不同污染源貢獻比例及臭氧光化反應潛勢值及生成潛勢，同時配合實場觀測採樣結果，由光化指標界定於不同污染物控制下，以MIR理論探討不同光化指標之臭氧生成潛勢。
研究結果顯示光化測站中各VOCs以濃度而言，朴子站及台西站主要貢獻為烷類，其次為芳香烴類。以MIR及Prop-equiv而言，朴子站主要貢獻為芳香烴類(53%)，其次為烯類(26%)，台西站則是以芳香烴類與烯類兩者為主要貢獻。以VOCs年平均濃度進行排序而言，朴子地區與台西地區排名首要物種為Toluene；以MIR排序而言朴子地區首要物種為Toluene，其次為m,p-Xylene，台西地區為Ethylene，其次為Propylene；以丙烯當量濃度排序結果，朴子地區首要為m,p-Xylene，其次為Isoprene，台西地區首要物種為Propylene，其次為Toluene，但於朴子地區與台西地區主要前十名物種有Isopentane、Ethylene、Propylene、Isoprene、1-Butene、cis-2-Butene、1,2,4-trimethylbenzene、Toluene、m,p-Xylene、o-Xylene。而這些VOCs物種與六輕離島工業區之廢氣燃燒塔流量比較而言，朴子地區顯示與流量較不相關，然而台西地區Ethylene及Propylene顯示在任何流量下日間都有高濃度值，Isoprene物種濃度出現之時間反而於清晨夜間時段出現。
本研究CMB受體模式分析中，選擇半生期大於12小時之物種以及不確定比例25%下進行模擬，臭氧事件日時段各污染源貢獻比例移動源(汽機車廢氣、油氣揮發)為48%，石油煉製業34.4%，溶劑使用(工業溶劑塗裝、印刷業與汽車保養維修業)為12.7%，塑膠製品製造業為3.9%，生物源1.6%；非事件日中移動源(汽機車廢氣、油氣揮發)為51.2%，石油煉製業24.4%，溶劑使用(工業溶劑塗裝、印刷業與汽車保養維修業)為18.6%，塑膠製品製造業為4.6%，生物源1.2%；以Mann-Whitney- Wilcbxon方法進行檢定結果顯示，以二行程機車廢氣、油氣揮發、石油煉製業與溶劑使用污染源有顯著差異，其中事件日比例高於非事件日為石油煉製業與油氣揮發於2007年差異比例分別為8.5%與3.7%，2008年分別為7.4%與2.1%。
不同污染源以臭氧生成潛勢結果分析結果，於臭氧事件日中移動源(汽機車廢氣、油氣揮發)貢獻比例為46.8%，石油煉製業28.2%，溶劑使用(工業溶劑塗裝、印刷業與汽車保養維修業)為16%，塑膠製品製造業為2.6%，生物源6.4%；非事件日中移動源(汽機車廢氣、油氣揮發)貢獻比例為54.2%，石油煉製業22%，溶劑使用(工業溶劑塗裝、印刷業與汽車保養維修業)為17.6%，塑膠製品製造業為2.4%，生物源3.7%。以Mann-Whitney- Wilcbxon方法進行檢定結果顯示石油煉製業污染源於事件日貢獻比例高於非事件日時段達14%。各污染源臭氧光化反應潛勢值(ppb -O3/ppb-VOCs)，以生物源4.67(ppb -O3/ppb-VOCs)最高，同時在事件日與非事件日中除石油煉製業外，其餘污染源至少1(ppb -O3/ppb-VOCs)以上；在事件日中，則可以發現石油煉製業污染源於事件日中其光化反應潛勢值(0.94ppb -O3/ppb-VOCs)高於非事件日(0.56~0.61 ppb -O3/ppb-VOCs)。

According to the monitoring data, the major air pollutant responsible for PSI above 100 was O3 in Yun-Chia-Nan air basin. In this study, the VOCs characteristics in Chiayi were evaluated by means of three indices: annual average mass concentrations, maximum incremental reactivity (MIR) and propylene-equivalent concentrations. The contributions of the volatile organic compounds during ozone and non-ozone episodes from various emission sources were estimated by chemical mass balance receptor model (CMB8.2). Finally, MIR was used to evaluate ozone formation potential by various emission sources.
For mass concentrations, alkane was the major contributor and main compound was toluene at Puzih and Taisi. For MIR and Prop-equiv, aromatics and alkenes account for 53% and 26% at Puzih and 40% at Taisi. Toluene and m,p-Xylene were found to have the highest ozone formation potential (OFP) and OH-reactivity at Puzih and ethylene and propylene at Taisi. Compared with flare flows in industrial park, there have no trend at Puzih. On the contrary, alkenes usually have high concentrations in the noontime at Taisi.
The receptor model estimated the mobile source (48%) was the largest source of VOCs during ozone episodes, followed by the petroleum refinery (34.4%), solvent use (12.7%), plastic manufacturers (3.9%) and biological emission (1.6%). During non-ozone epiodes, the mobile source (51.2%) was also the largest source of VOCs, followed by the petroleum refinery (24.4%), solvent use (18.6%), plastic manufacturers (4.6%) and biological emission (1.2%). The paired-sample nonparamentric statistics is applied to individual sources for evaluating differences between ozone and non-ozone episodes. A significant difference was found for the petroleum refinery and gasoline evaporation, amounting 8.5% and 2.7% in 2007, 7.4% and 2.1% in 2008, respectively.
The mobile source (46.8%) was the largest contributors to OFPs, followed by the petroleum refinery (28.3%), solvent use (16%), plastic manufacturers (2.6%) and biological emission (6.4%) during episodes. However, the OFPs of contribution percentages during non-ozone episodes were mobile source (54.2%), petroleum refinery (22%), solvent use (17.6%), plastic manufacturers (2.4%) and biological emission (3.7%) during non-ozone episodes. The paired-sample nonparamentric statistics test shows petroleum refinery had a significant difference and the largest difference in percentage between ozones and non-ozone episodes is 14%. The highest average ozone increment among the various sources is biological emission (4.67ppb -O3/ppb-VOCs). The effect of petroleum refinery was different between ozone (0.94ppb -O3/ppb-VOCs) and non-ozone episodes (0.56~0.61ppb -O3/ppb -VOCs).

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